CN112861378B - Improved Husky algorithm-based boosting section flight program optimization method and device - Google Patents

Abstract

The application relates to a boosting section flight program optimization method and device based on an improved wolf algorithm, computer equipment and a storage medium. The method comprises the following steps: constructing a ballistic missile motion model and obtaining an attack angle model of a missile boosting section, taking an attack angle characteristic parameter as a position vector of the gray wolf, and initializing a gray wolf population; the method comprises the steps of improving a linear convergence factor in a classical grey wolf algorithm into a nonlinear convergence factor with slower attenuation in the early stage of iteration and faster attenuation in the later stage of iteration, optimizing attack angle characteristic parameters through the improved grey wolf algorithm, updating a position vector according to a fitness function of terminal state deviation, iterating until the maximum iteration times are reached, and obtaining a standard trajectory of a trajectory missile motion model according to the position vector of the optimal grey wolf. According to the method, the key parameters of the attack angle model are used as optimization variables, the minimum terminal state deviation is used as an optimization target, the standard trajectory meeting the constraint is iteratively optimized, and the generation precision of the standard trajectory of the boosting section of the solid missile is further improved.

Description

A method and device for optimizing flight procedures in boost phase based on improved gray wolf algorithm

technical field

The present application relates to the technical field of ballistic planning, in particular to a method, device, computer equipment and storage medium for optimizing the flight program of the boost phase based on the improved gray wolf algorithm.

Background technique

Ballistic missiles have outstanding features such as long range, high precision, high power, and strong destructive power. They are a weapon with strong offensive and deterrent capabilities, and are also an important pillar for defending homeland security and maintaining regional strategic balance. Most of the flight trajectories of ballistic missiles are bound offline. Therefore, it is of great significance to design a standard ballistic trajectory that meets the needs of the battlefield and can complete the specified tasks.

In the prior art, there is a problem that the ballistic design accuracy is not high enough.

Contents of the invention

Based on this, it is necessary to address the above technical problems and provide a flight program optimization method, device, computer equipment and storage medium based on the improved gray wolf algorithm for boosting flight procedures that can improve the accuracy of trajectory design.

A method for optimizing the flight procedure of the boost segment based on the improved gray wolf algorithm, the method comprising:

Construct the ballistic missile motion model and obtain the attack angle model of the missile boosting phase;

Using the angle-of-attack characteristic parameter in the angle-of-attack model as the position vector of the gray wolf in the gray wolf algorithm, initialize the gray wolf population according to the preset value range;

The linear convergence factor in the classic gray wolf algorithm is improved to a nonlinear convergence factor, and the improved gray wolf algorithm is obtained; the decay rate of the nonlinear convergence factor at the moment before the iteration is smaller than the decay rate at the moment after the iteration;

Optimize the characteristic parameters of the angle of attack through the improved gray wolf algorithm, and update the position vector according to the fitness function of the terminal state deviation, iterate until the maximum number of iterations is reached, and output the global optimal gray wolf, according to the The position vector of the optimal gray wolf is used to obtain the standard trajectory of the ballistic missile motion model.

In one of the embodiments, it also includes: building a ballistic missile motion model and obtaining the angle-of-attack model of the missile boosting section as:

t_m表示α₁对应时刻；α₁、α₂、α₃、α₄分别为第一至四次负攻角转弯的最小值。Among them, α(t) represents the angle of attack at time t; t ₀ , t ₁₁ represent the start and end times of the vertical takeoff segment; t ₁₂ , t ₁₃ represent the start and end time of the transonic segment; t _1f , t ₂₀ , t _2f , t ₃₀ , t _3f represent the start and end time of the interstage separation section; t ₂₀ , t _2f represent the start and end time of the second-stage flight segment of the missile; t ₃₀ , t _f represent the start and end time of the third-stage flight segment of the missile;

t _m represents the time corresponding to α ₁ ; α ₁ , α ₂ , α ₃ , and α ₄ are the minimum values of the first to fourth negative angle-of-attack turns, respectively.

In one of the embodiments, it also includes: using the characteristic parameter of the angle of attack in the angle of attack model as the position vector of the gray wolf in the gray wolf algorithm; the characteristic parameter of the angle of attack is the minimum value of four negative angle of attack turns;

A random value is generated within a preset value range, and the population of gray wolves is initialized according to the random value.

In one of the embodiments, it also includes: improving the linear convergence factor in the classic gray wolf algorithm to a nonlinear convergence factor to obtain an improved gray wolf algorithm; the nonlinear convergence factor is:

Among them, a is the nonlinear convergence factor; t is the current iteration number; e is a natural number; max is the maximum iteration number.

In one of the embodiments, the method further includes: obtaining the current terminal state of the missile according to the characteristic parameters of the angle of attack; the terminal state of the missile includes the terminal height, speed and trajectory inclination of the missile.

In one of the embodiments, it also includes: obtaining a preset target terminal state;

According to the position vector of each gray wolf in the current gray wolf population, the current terminal state corresponding to the position vector is obtained;

Obtaining a terminal state deviation according to the target terminal state and the current terminal state;

Find the position vector that minimizes the fitness function of the terminal state deviation as the optimization result;

Updating the position vector of the gray wolf population according to the optimization result.

In one of the embodiments, it also includes: optimizing the characteristic parameters of the angle of attack through the improved gray wolf algorithm, and updating the position vector according to the fitness function of the terminal state deviation, iterating until the maximum number of iterations is reached , output the global optimal gray wolf; the fitness function of the terminal state deviation is:

Among them, Fitness represents the fitness function value of the terminal state deviation; H _f , V _f , θ _f represent the target terminal height, velocity and ballistic inclination; H _present , V _present , θ _present represent the values obtained after each optimization calculation ΔH, ΔV, and Δθ represent the maximum allowable deviation of the preset height, speed and trajectory inclination.

A device for optimizing flight procedures in the boost phase based on the improved gray wolf algorithm, said device comprising:

The angle-of-attack model determination module is used to construct the motion model of the ballistic missile and obtain the angle-of-attack model of the missile boosting section;

The gray wolf population initialization module is used to use the angle-of-attack characteristic parameter in the angle-of-attack model as the position vector of the gray wolf in the gray wolf algorithm, and initialize the gray wolf population according to a preset value range;

The convergence factor improvement module is used to improve the linear convergence factor in the classic gray wolf algorithm to a nonlinear convergence factor to obtain an improved gray wolf algorithm; the decay rate of the nonlinear convergence factor at the moment before the iteration is smaller than that at the moment after the iteration Decay speed;

The iteration module is used to optimize the characteristic parameters of the angle of attack through the improved gray wolf algorithm, and update the position vector according to the fitness function of the terminal state deviation, iterate until the maximum number of iterations is reached, and output the global optimum gray wolf, obtaining the standard trajectory of the ballistic missile motion model according to the position vector of the optimal gray wolf.

A computer device, comprising a memory and a processor, the memory stores a computer program, and the processor implements the following steps when executing the computer program:

Construct the ballistic missile motion model and obtain the attack angle model of the missile boosting phase;

Using the angle-of-attack characteristic parameter in the angle-of-attack model as the position vector of the gray wolf in the gray wolf algorithm, initialize the gray wolf population according to the preset value range;

The linear convergence factor in the classic gray wolf algorithm is improved to a nonlinear convergence factor, and the improved gray wolf algorithm is obtained; the nonlinear convergence factor decays slowly in the early stage of iteration, and decays faster in the later stage of iteration;

Optimize the characteristic parameters of the angle of attack through the improved gray wolf algorithm, and update the position vector according to the fitness function of the terminal state deviation, iterate until the maximum number of iterations is reached, and output the global optimal gray wolf, according to the The position vector of the optimal gray wolf is used to obtain the standard trajectory of the ballistic missile motion model.

A computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

Construct the ballistic missile motion model and obtain the attack angle model of the missile boosting phase;

Using the angle-of-attack characteristic parameter in the angle-of-attack model as the position vector of the gray wolf in the gray wolf algorithm, initialize the gray wolf population according to the preset value range;

The linear convergence factor in the classic gray wolf algorithm is improved to a nonlinear convergence factor, and the improved gray wolf algorithm is obtained; the decay rate of the nonlinear convergence factor at the moment before the iteration is smaller than the decay rate at the moment after the iteration;

Optimize the characteristic parameters of the angle of attack through the improved gray wolf algorithm, and update the position vector according to the fitness function of the terminal state deviation, iterate until the maximum number of iterations is reached, and output the global optimal gray wolf, according to the The position vector of the optimal gray wolf is used to obtain the standard trajectory of the ballistic missile motion model.

The above-mentioned method, device, computer equipment and storage medium for boosting flight program optimization based on the improved gray wolf algorithm, by constructing the ballistic missile motion model and obtaining the attack angle model of the missile boosting section; the angle of attack characteristic parameters in the angle of attack model As the position vector of the gray wolf in the gray wolf algorithm, the gray wolf population is initialized according to the preset value range; The linear convergence factor is used to obtain the improved gray wolf algorithm; through the improved gray wolf algorithm, the characteristic parameters of the angle of attack are optimized, and the position vector is updated according to the fitness function of the terminal state deviation, iterated until the maximum number of iterations is reached, and the global optimum is output Gray wolf, according to the position vector of the optimal gray wolf, the standard trajectory of the ballistic missile motion model is obtained. In the present invention, the key parameters of the angle of attack model are used as optimization variables, and the minimum terminal state deviation is used as the optimization target to iteratively optimize the standard trajectory satisfying the constraints, thereby further improving the generation accuracy of the standard trajectory in the boosting section of the solid missile.

Description of drawings

Fig. 1 is a schematic flow chart of the booster section flight program optimization method based on the improved gray wolf algorithm in one embodiment;

Fig. 2 is a schematic diagram of pitch program angle in one embodiment;

Fig. 3 is a schematic diagram of angle of attack variation in one embodiment;

Fig. 4 is a comparison diagram of the convergence factor in the classic gray wolf algorithm and an embodiment;

Fig. 5 is the variation curve of the main ballistic parameter curve and fitness value of standard ballistic in an embodiment;

Fig. 6 is a schematic diagram of a booster segment flight program optimization device based on the improved gray wolf algorithm in one embodiment;

Figure 7 is an internal block diagram of a computer device in one embodiment.

Detailed ways

In order to make the purpose, technical solution and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present application, and are not intended to limit the present application.

The boost flight procedure optimization method based on the improved gray wolf algorithm provided by this application can be applied in the following application environments. Construct the ballistic missile motion model and obtain the attack angle model of the missile booster; use the attack angle characteristic parameters in the attack angle model as the gray wolf position vector in the gray wolf algorithm, and initialize the gray wolf population according to the preset value range; The linear convergence factor in the classic gray wolf algorithm is improved to a nonlinear convergence factor that decays slowly in the early stage of iteration and fast in the late iteration, and the improved gray wolf algorithm is obtained; the characteristic parameters of the angle of attack are searched through the improved gray wolf algorithm. Optimal, and update the position vector according to the fitness function of the terminal state deviation, iterate until the maximum number of iterations is reached, output the global optimal gray wolf, and obtain the standard trajectory of the ballistic missile motion model according to the position vector of the optimal gray wolf.

In one embodiment, as shown in Figure 1, a kind of boosting section flight program optimization method based on the improved gray wolf algorithm is provided, comprising the following steps:

Step 102, building a motion model of the ballistic missile and obtaining an angle of attack model of the boosting phase of the missile.

In the present invention, the earth is regarded as a homogeneous sphere, the influence of the control force is ignored, and the zero-tilt flight of the axisymmetric missile is considered, the simplified model of the ballistic missile motion model can be obtained as follows:

为高度变化率；

为地心距变化率；

为速度变化率；

为弹道倾角变化率；

为秒耗量；P为沿弹体方向推力；m为质量；g为引力加速度；D为阻力；L为升力；r为地心距。Among them, θ is the ballistic inclination angle, υ is the roll angle, α is the attack angle, and β is the sideslip angle.

is the height change rate;

is the change rate of the earth center distance;

is the velocity change rate;

is the rate of change of ballistic inclination;

P is the thrust in the direction of the projectile; m is the mass; g is the gravitational acceleration; D is the resistance; L is the lift; r is the distance from the center of the earth.

表示俯仰程序角。其中，t₀-t₁₁为垂直起飞段，t₁₂-t₁₃为跨声速段，为控制导弹终端高度，在第一级选择采用二次转弯的方式。t_1f-t₂₀、t_2f-t₃₀为级间分离段，t₂₀-t_2f为导弹第二级飞行段，t₃₀-t_f为导弹第三级飞行段。为实现上图中的俯仰飞行程序角，设计满足终端条件的攻角变化形式如图3，图中，α表示攻角。According to the movement law of the missile booster section, the design pitch angle changes with time as shown in Figure 2. In the figure,

Indicates the pitch program angle. Among them, t ₀ -t ₁₁ is the vertical take-off section, and t ₁₂ -t ₁₃ is the transonic section. In order to control the height of the missile terminal, a second turn is chosen in the first stage. t _1f -t ₂₀ , t _2f -t ₃₀ are inter-stage separation sections, t ₂₀ -t _2f is the second-stage flight section of the missile, and t ₃₀ -t _f is the third-stage flight section of the missile. In order to realize the pitching flight program angle in the figure above, the change form of the angle of attack that satisfies the terminal condition is designed as shown in Figure 3. In the figure, α represents the angle of attack.

By calculating the key parameters of the angle-of-attack model to determine the change law of the attack angle, and then using the Euler angle transformation relationship to obtain the change law of the pitch angle, the problem of obtaining the standard trajectory is transformed into a parameter optimization problem.

In step 104, the characteristic parameter of the angle of attack in the angle of attack model is used as the position vector of the gray wolf in the gray wolf algorithm, and the gray wolf population is initialized according to a preset value range.

The Gray Wolf Algorithm is a swarm intelligence algorithm. It is a relatively novel swarm intelligence algorithm proposed by Seyedali and other scholars in 2014. The Gray Wolf Algorithm simulates the hierarchy and hunting behavior of gray wolves in nature. The entire gray wolf population is divided into α , β, δ, ω four groups, the first three groups are the three groups with the best fitness, and these three groups guide the wolves of the fourth group to think about the target search. During the optimization process, the wolves update α, β, δ , the position of ω. In the present invention, each gray wolf is a four-dimensional vector representing a set of solutions of four key parameters, wherein each parameter is the four eigenvalues of the angle of attack shown in Figure 3, and the initial value of the gray wolf's position is the corresponding search Random values generated within the spatial extent.

Step 106, improving the linear convergence factor in the classic gray wolf algorithm to a nonlinear convergence factor to obtain an improved gray wolf algorithm; the decay rate of the nonlinear convergence factor at the moment before the iteration is smaller than that at the moment after the iteration.

In the classic gray wolf algorithm, the update method of the gray wolf individual is:

X(t+1)=X(t)-A·D

收敛因子a是随着迭代次数从2现行递减到0。当|A|＞1时，灰狼群体扩大搜索范围，对应于全局搜索，当|A|＜1时，灰狼群体将包围圈缩小，对应于局部搜索。Among them, X represents the position vector of the gray wolf, t represents the current iteration number, D represents the distance between the individual and the target, A represents the control search area, A=2a r ₂ -a, r ₂ is in the range of [0,1] Random number, a represents the iteration factor,

The convergence factor a decreases from 2 to 0 with the number of iterations. When |A|>1, the gray wolf group expands the search range, which corresponds to the global search; when |A|<1, the gray wolf group narrows the encirclement, which corresponds to the local search.

The convergence factor a of the classic gray wolf algorithm decreases linearly from 2 to 0, and the rate of global search in the early stage is consistent with the rate of local search in the later stage, which will reduce the search efficiency to a certain extent. In order to improve the algorithm efficiency when the gray wolf algorithm is applied to the scene of the present invention, the present invention proposes a nonlinear convergence factor that decays slowly in the early stage of the iteration and decays faster in the later stage of the iteration, and its expression is:

The variation of the convergence factor of the classic gray wolf algorithm and the improved gray wolf algorithm in the present invention with the number of iterations is shown in Figure 4. It can be seen that the slope of the improved nonlinear convergence factor is higher than that of the iteration factor slope of the classic gray wolf algorithm at the initial stage of iteration. Low, the search speed slows down, and there is more time to find the optimal area, and in the later stage of the iteration, the slope becomes higher rapidly, which means that after the optimal direction is found, the search speed is increased and the optimal solution can be found faster.

Step 108, optimize the characteristic parameters of the angle of attack through the improved gray wolf algorithm, and update the position vector according to the fitness function of the terminal state deviation, iterate until the maximum number of iterations is reached, and output the global optimal gray wolf, according to the optimal gray wolf The position vector of gets the standard trajectory of the ballistic missile motion model.

By calculating the terminal state deviation, find the position vector corresponding to the minimum value of the deviation of the terminal state in each iteration, that is, the four eigenvalues of the angle of attack model, and update the values of α, β, and δ in the loop at the same time. With the accumulation of iterations, the positions of gray wolves at each level will gradually approach the target, and the optimal fitness value will become smaller and smaller. Ideally, through continuous iterative calculation, the final optimal fitness value will tend to zero. At this time, the optimal solution that meets the requirements is found. According to the optimal solution of the four attack angle eigenvalues, the corresponding standard trajectory can be obtained by substituting the motion model of the ballistic missile.

In the above-mentioned flight program optimization method for the boost phase based on the improved gray wolf algorithm, the ballistic missile motion model is constructed and the angle of attack model of the missile boost phase is obtained; the characteristic parameters of the angle of attack in the angle of attack model are used as the gray wolf The position vector of the gray wolf is initialized according to the preset value range; the linear convergence factor in the classic gray wolf algorithm is improved to a nonlinear convergence factor that decays slowly in the early stage of the iteration and decays quickly in the late iteration, and the improved Gray wolf algorithm: Optimize the characteristic parameters of the angle of attack through the improved gray wolf algorithm, and update the position vector according to the fitness function of the terminal state deviation, iterate until the maximum number of iterations is reached, and output the global optimal gray wolf, according to the optimal gray wolf The position vector of the wolf gets the standard trajectory of the ballistic missile motion model. In the present invention, the key parameters of the angle of attack model are used as optimization variables, and the minimum terminal state deviation is used as the optimization target to iteratively optimize the standard trajectory satisfying the constraints, thereby further improving the generation accuracy of the standard trajectory in the boosting section of the solid missile.

In one of the embodiments, it also includes: building a ballistic missile motion model and obtaining the angle-of-attack model of the missile boosting section as:

t_m表示α₁对应时刻；α₁、α₂、α₃、α₄分别为第一至四次负攻角转弯的最小值。Among them, α(t) represents the angle of attack at time t; t ₀ , t ₁₁ represent the start and end times of the vertical takeoff segment; t ₁₂ , t ₁₃ represent the start and end time of the transonic segment; t _1f , t ₂₀ , t _2f , t ₃₀ , t _3f represent the start and end time of the interstage separation section; t ₂₀ , t _2f represent the start and end time of the second-stage flight segment of the missile; t ₃₀ , t _f represent the start and end time of the third-stage flight segment of the missile;

t _m represents the time corresponding to α ₁ ; α ₁ , α ₂ , α ₃ , and α ₄ are the minimum values of the first to fourth negative angle-of-attack turns, respectively.

In one of the embodiments, it also includes: using the angle-of-attack characteristic parameter in the angle-of-attack model as the position vector of the gray wolf in the gray wolf algorithm; the angle-of-attack characteristic parameter is the minimum value of four negative angle-of-attack turns; Random values are generated within the value range, and the gray wolf population is initialized according to the random values.

In one of the embodiments, it also includes: improving the linear convergence factor in the classic gray wolf algorithm to a nonlinear convergence factor to obtain an improved gray wolf algorithm; the nonlinear convergence factor is:

Among them, a is the nonlinear convergence factor; t is the current iteration number; e is a natural number; max is the maximum iteration number.

The characteristic of the nonlinear convergence factor in this embodiment is that the slope range is relatively large, and the slope value is very small in the early stage of the iteration, leaving sufficient time for the global search, and the slope value rapidly increases in the later stage of the iteration, speeding up the speed of the local search. It meets the requirements of the flight procedure optimization scene of the present invention.

In one of the embodiments, it further includes: obtaining the current terminal state of the missile according to the characteristic parameters of the angle of attack; the terminal state of the missile includes the terminal height, speed and trajectory inclination of the missile.

In one of the embodiments, it also includes: obtaining the preset target terminal state; according to the position vector of each gray wolf in the current gray wolf population, obtaining the current terminal state corresponding to the position vector; the terminal state of the missile includes the terminal height and speed of the missile and the ballistic inclination angle; according to the target terminal state and the current terminal state, the terminal state deviation is obtained; the position vector that minimizes the fitness function of the terminal state deviation is found as the optimization result; the fitness function of the terminal state deviation is:

Among them, Fitness represents the fitness function value of the terminal state deviation; H _f , V _f , θ _f represent the target terminal height, velocity and ballistic inclination; H _present , V _present , θ _present represent the current Terminal height, speed and ballistic inclination; ΔH, ΔV, Δθ represent the maximum allowable deviation of preset height, speed and ballistic inclination.

Update the position vector of the gray wolf population according to the optimization results; iterate until the maximum number of iterations is reached, and output the global optimal gray wolf.

其中，

是一个给定的速度值，用于归一化脱密处理。在求解过程中，为了保证计算精度，灰狼数量的设定不宜过少，另一方面，考虑到尽量提高计算效率，灰狼数量的设定也不宜过多，且迭代循环的最大次数同样不宜过高。因此经过综合考虑，取种群规模为20，最大迭代次数为20次。灰狼的位置初值为相应搜索空间范围内生成的随机值，设定攻角初始搜索空间如表1所示。In a specific embodiment, the terminal height of the designed initial trajectory is h _f =90km, the terminal trajectory inclination angle θ _f =0°, and the terminal velocity is

in,

is a given velocity value for normalized declassification. In the process of solving, in order to ensure the calculation accuracy, the number of gray wolves should not be too small. On the other hand, considering improving the calculation efficiency as much as possible, the number of gray wolves should not be too large, and the maximum number of iteration cycles should not be too large. too high. Therefore, after comprehensive consideration, the population size is 20, and the maximum number of iterations is 20. The initial value of the gray wolf's position is a random value generated within the corresponding search space, and the initial search space of the angle of attack is set as shown in Table 1.

Table 1 Search space of angle of attack eigenvalues

After iterative calculation, the standard ballistic trajectory that can satisfy the terminal constraints and process constraints is obtained. The four attack angle eigenvalues obtained by simulation are shown in Table 2, which are α ₁ =-13.3358°, α ₂ =-6.5173°, α ₃ =- 17°, α ₄ =-20.4°, Fig. 5 shows the curves of the main ballistic parameters and the variation curves of the fitness value of the standard ballistic obtained by simulation, where the speed is normalized in the simulation results.

Table 2 Simulation results of eigenvalues of angle of attack

It can be seen from the simulation results that the terminal height obtained by using the gray wolf algorithm to design the initial trajectory is 89.84km, the terminal trajectory inclination angle is -0.1727°, the terminal velocity deviation is 17m/s, and the terminal state deviation does not exceed the deviation index constraints, and by It can be seen from the figure that during the flight of the whole boost phase, the change curves of altitude, speed and ballistic inclination are relatively gentle. In addition, the change form of the angle of attack is consistent with the design law, and the process constraints also meet the given index requirements. It can be seen from Figure 5(g) that when the iteration reaches the seventh time, the optimal fitness value is already less than 1, and the algorithm has found the optimal solution that can satisfy the process constraints and terminal constraints, thus It can be seen that the standard ballistic design method based on gray wolf algorithm has better accuracy and calculation efficiency.

It should be understood that although the various steps in the flow chart of FIG. 1 are displayed sequentially as indicated by the arrows, these steps are not necessarily executed sequentially in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least some of the steps in Fig. 1 may include multiple sub-steps or multiple stages, these sub-steps or stages are not necessarily executed at the same time, but may be executed at different times, the execution of these sub-steps or stages The order is not necessarily performed sequentially, but may be performed alternately or alternately with at least a part of other steps or sub-steps or stages of other steps.

In one embodiment, as shown in FIG. 6 , a device for optimizing the flight program of the boost phase based on the improved gray wolf algorithm is provided, including: an angle of attack model determination module 602, a gray wolf population initialization module 604, and a convergence factor improvement module 606 and iteration module 608, wherein:

The angle of attack model determination module 602 is used to construct the motion model of the ballistic missile and obtain the angle of attack model of the missile boosting section;

The gray wolf population initialization module 604 is used to use the angle-of-attack characteristic parameter in the angle-of-attack model as the position vector of the gray wolf in the gray wolf algorithm, and initialize the gray wolf population according to a preset value range;

The convergence factor improvement module 606 is used to improve the linear convergence factor in the classic gray wolf algorithm to a nonlinear convergence factor to obtain an improved gray wolf algorithm; the decay rate of the nonlinear convergence factor at the moment before the iteration is smaller than that at the moment after the iteration speed;

The iteration module 608 is used to optimize the characteristic parameters of the angle of attack through the improved gray wolf algorithm, and update the position vector according to the fitness function of the terminal state deviation, iterate until the maximum number of iterations is reached, and output the global optimal gray wolf. The position vector of the optimal gray wolf gets the standard trajectory of the ballistic missile motion model.

The gray wolf population initialization module 604 is also used to use the angle-of-attack characteristic parameter in the angle-of-attack model as the position vector of the gray wolf in the gray wolf algorithm; the angle-of-attack characteristic parameter is the minimum value of four negative angle-of-attack turns; Random values are generated within the value range, and the gray wolf population is initialized according to the random values.

The convergence factor improvement module 606 is also used to obtain the preset target terminal state; according to the position vector of each gray wolf in the current gray wolf population, the current terminal state corresponding to the position vector is obtained; according to the target terminal state and the current terminal state, the terminal state is obtained Deviation; find the position vector that minimizes the fitness function of the terminal state deviation as the optimization result; update the position vector of the gray wolf population according to the optimization result.

For the specific limitations of the booster flight program optimization device based on the improved gray wolf algorithm, please refer to the above definition of the booster flight program optimization method based on the improved gray wolf algorithm, and will not be repeated here. Each module in the above-mentioned booster flight program optimization device based on the improved gray wolf algorithm can be fully or partially realized by software, hardware and combinations thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, and can also be stored in the memory of the computer device in the form of software, so that the processor can invoke and execute the corresponding operations of the above-mentioned modules.

In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure may be as shown in FIG. 7 . The computer device includes a processor, a memory, a network interface, a display screen and an input device connected through a system bus. Wherein, the processor of the computer device is used to provide calculation and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a method for optimizing the flight program of the boosting segment based on the improved gray wolf algorithm is realized. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a button, a trackball or a touch pad provided on the casing of the computer device , and can also be an external keyboard, touchpad, or mouse.

Those skilled in the art can understand that the structure shown in Figure 7 is only a block diagram of a part of the structure related to the solution of this application, and does not constitute a limitation to the computer equipment on which the solution of this application is applied. The specific computer equipment can be More or fewer components than shown in the figures may be included, or some components may be combined, or have a different arrangement of components.

In one embodiment, a computer device is provided, including a memory and a processor, the memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the steps in the foregoing method embodiments are implemented.

Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be implemented through computer programs to instruct related hardware, and the computer programs can be stored in a non-volatile computer-readable memory In the medium, when the computer program is executed, it may include the processes of the embodiments of the above-mentioned methods. Wherein, any references to memory, storage, database or other media used in the various embodiments provided in the present application may include non-volatile and/or volatile memory. Nonvolatile memory can include read only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Chain Synchlink DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

The technical features of the above embodiments can be combined arbitrarily. To make the description concise, all possible combinations of the technical features in the above embodiments are not described. However, as long as there is no contradiction in the combination of these technical features, they should be It is considered to be within the range described in this specification.

The above-mentioned embodiments only represent several implementation modes of the present application, and the description thereof is relatively specific and detailed, but it should not be construed as limiting the scope of the patent for the invention. It should be noted that those skilled in the art can make several modifications and improvements without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the scope of protection of the patent application should be based on the appended claims.

Claims (7)

1. A method for optimizing the flight procedure of boosting section based on the improved gray wolf algorithm, characterized in that, the method comprises: Construct the ballistic missile motion model and obtain the attack angle model of the missile boosting phase; The angle-of-attack characteristic parameter in the described angle-of-attack model is used as the position vector of the gray wolf in the gray wolf algorithm, and the gray wolf population is initialized according to a preset value range; value; The linear convergence factor in the classic gray wolf algorithm is improved to a nonlinear convergence factor, and the improved gray wolf algorithm is obtained; the decay rate of the nonlinear convergence factor at the moment before the iteration is smaller than the decay rate at the moment after the iteration; the nonlinear The convergence factor is:

Among them, a is the nonlinear convergence factor; t is the current iteration number; e is a natural number; max is the maximum iteration number; Optimize the characteristic parameters of the angle of attack through the improved gray wolf algorithm, and update the position vector according to the fitness function of the terminal state deviation, iterate until the maximum number of iterations is reached, and output the global optimal gray wolf, according to the The position vector of the optimal gray wolf obtains the standard trajectory of the ballistic missile motion model; the fitness function of the terminal state deviation is:

Among them, Fitness represents the fitness function value of the terminal state deviation; H _f , V _f , θ _f represent the target terminal height, velocity and ballistic inclination; H _present , V _present , θ _present represent the values obtained after each optimization calculation ΔH, ΔV, and Δθ represent the maximum allowable deviation of the preset height, speed and trajectory inclination. 2. method according to claim 1, is characterized in that, described building ballistic missile motion model and obtaining the angle of attack model of missile boosting section comprises: Construct the ballistic missile motion model and obtain the attack angle model of the missile boost phase as follows:

Among them, α(t) represents the angle of attack at time t; t ₀ , t ₁₁ represent the start and end times of the vertical takeoff segment; t ₁₂ , t ₁₃ represent the start and end time of the transonic segment; t _1f , t ₂₀ , t _2f , t ₃₀ , t _3f represent the start and end time of the interstage separation section; t ₂₀ , t _2f represent the start and end time of the second stage flight segment of the missile; t ₃₀ , t _3f represent the start and end time of the third stage flight segment of the missile;

t _m represents the time corresponding to α ₁ ; α ₁ , α ₂ , α ₃ , and α ₄ are the minimum values of the first to fourth negative angle-of-attack turns, respectively. 3. The method according to claim 2, wherein, before updating the position vector according to the fitness function of the terminal state deviation, further comprising: The current terminal state of the missile is obtained according to the characteristic parameters of the angle of attack; the terminal state of the missile includes the terminal height, speed and trajectory inclination of the missile. 4. The method according to claim 3, characterized in that, the angle of attack characteristic parameter is optimized by the improved gray wolf algorithm, and the position vector is updated according to the fitness function of the terminal state deviation, comprising : Obtain the preset target terminal state; According to the position vector of each gray wolf in the current gray wolf population, the current terminal state corresponding to the position vector is obtained; Obtaining a terminal state deviation according to the target terminal state and the current terminal state; Find the position vector that minimizes the fitness function of the terminal state deviation as the optimization result; Updating the position vector of the gray wolf population according to the optimization result. 5. A booster segment flight program optimization device based on the improved gray wolf algorithm, characterized in that the device comprises: The angle-of-attack model determination module is used to construct the motion model of the ballistic missile and obtain the angle-of-attack model of the missile boosting section; The gray wolf population initialization module is used to use the angle-of-attack characteristic parameter in the described angle-of-attack model as the position vector of the gray wolf in the gray wolf algorithm, and initializes the gray wolf population according to a preset value range; the characteristic angle-of-attack parameter is Minimum of four negative AOA turns; The convergence factor improvement module is used to improve the linear convergence factor in the classic gray wolf algorithm to a nonlinear convergence factor to obtain an improved gray wolf algorithm; the decay rate of the nonlinear convergence factor at the moment before the iteration is smaller than that at the moment after the iteration Decay speed; the nonlinear convergence factor is:

Among them, a is the nonlinear convergence factor; t is the current iteration number; e is a natural number; max is the maximum iteration number; The iteration module is used to optimize the characteristic parameters of the angle of attack through the improved gray wolf algorithm, and update the position vector according to the fitness function of the terminal state deviation, iterate until the maximum number of iterations is reached, and output the global optimum Gray wolf, obtain the standard trajectory of the ballistic missile motion model according to the position vector of the optimal gray wolf; the fitness function of the terminal state deviation is:

Among them, Fitness represents the fitness function value of the terminal state deviation; H _f , V _f , θ _f represent the target terminal height, velocity and ballistic inclination; H _present , V _present , θ _present represent the values obtained after each optimization calculation ΔH, ΔV, and Δθ represent the maximum allowable deviation of the preset height, speed and trajectory inclination. 6. A computer device, comprising a memory and a processor, the memory stores a computer program, wherein the processor implements the steps of the method according to any one of claims 1 to 4 when executing the computer program . 7. A computer-readable storage medium, on which a computer program is stored, characterized in that, when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 4 are realized.

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